# @awseventschannel on YouTube

- **Type:** Video
- **Original URL:** https://youtube.com/watch?v=_l0ilpzuLmI
- **Gondola URL:** https://gondola.cc/posts/66059427-awseventschannel-youtube
- **Thumbnail:** https://img.gondola.cc/tr:w-,h-,fo-auto/postThumbnails/ad1de80a7b.jpg
- **Posted:** 2026-05-29T05:44:57.000+00:00
- **Account Owner:** AWS Events (@awseventschannel) — https://gondola.cc/awseventschannel

## Caption

Your model isn't wrong—it's just looking at stale data.

Here's what happens in most ML teams: you compute features in a batch job for training, then cobble together a completely different system to serve them at inference time. The two drift apart. Your model silently degrades. Nobody knows why until a customer complains.

The fix isn't a better model. It's a better #Database.

🔹Use Amazon DynamoDB as a single-digit millisecond feature store that keeps training & inference in sync 🔹Walk through schema design for feature groups, versioning, & point-in-time lookups 🔹Build a metadata layer that tracks exactly which features, datasets, & hyperparameters went into every #MachineLearning model version

If you've ever shipped a model that worked in dev & slowly broke in production, this episode of Databases for #AI shows you where the real problem was hiding.

## Stats

- **Views:** 245
- **Likes:** 4
- **Shares:** 0
- **Comments:** 0

## Tags

machinelearning, ai, database

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